Abstract: In this study, multivariate statistical analysis was applied to characterize the flowability of different types of microcrystalline cellulose (MCC), and the visualization of R language was used to explore the intrinsic correlation on its performances. To verify the operability of multivariate statistical analysis, we compared the results of the conventional methods such as repose angle method, Hausner ratio method, Carr's index method and the parameter a of Kawakita equation to determine whether there are significant differences between the conventional ones and multivariate statistical analysis. Moreover, the fillibility and compressibility were characterized by parameters 1/b of Kawakita equation and the means of pressure-tensile strength and compressibility curve method, respectively. The data was analyzed through R language for visualizing the correlation among the performance parameters of MCC. The flowability of the series of microcrystalline cellulose PH (MCCPH) were superior to the series of microcrystalline cellulose WJ (MCCWJ), the compressibility of MCCPH-302 was optimum, and the flowability and fallibility of MCCPH-102 were better than others. The results of conventional methods were consistent with multivariate statistical analysis. The fillibility was positively correlated with flowability, both negatively correlated with compressibility by analyzing correlation coefficient diagram, which was statistically significant (P<0.01). It is reasonable that adopting multivariate statistical analysis to character the flowability of powders, which is more objective than the traditional approach. The correlation visualization of performance parameters of powders provides convenience for screening preparation material via the visualization of R language.